101–110 of 286 posts
Qwen3.5-122B-A10B BF16 GGUF = 224GB. The "80Gb VRAM" mentioned here will barely fit Q4_K_S (70GB), which will NOT perform as shown on benchmarks. Quite misleading, really.
I've been playing with Qwen3-Coder-Next and the Qwen3.5 models since they were each released.
They are impressive, but they are not performing at Sonnet 4.5 level in my experience.
I have observed that they're configured to be very tenacious. If you can carefully constrain the goal with some tests they need to pass and frame it in a way to keep them on track, they will just keep trying things over and over. They'll "solve" a lot of these problems in the way that a broken clock is right twice a day, but there's a lot of fumbling to get there.
That said, they are impressive for open source models. It's amazing what you can do with self-hosted now. Just don't believe the hype that these are Sonnet 4.5 level models because you're going to be very disappointed once you get into anything complex.
Theory is that some of the model parameters aren't set properly and this encourages endless looping behavior when run under ollama:
https://github.com/ollama/ollama/issues?q=is%3Aissue%20state... (a bunch of them)
If you're new to this: All of the open source models are playing benchmark optimization games. Every new open weight model comes with promises of being as good as something SOTA from a few months ago then they always disappoint in actual use. I've been playing with Qwen3-Coder-Next and the Qwen3.5 models since they were each released. They are impressive, but they are not performing at Sonnet 4.5 level in my experien…
And could quantization maybe partially explain the worse than expected results?
Earlier quoted context omitted.
Running local AI models on a laptop is a weird choice. The Mini and especially the Studio form factor will have better cooling, lower prices for comparable specs and a much higher ceiling in performance and memory capacity.
I can never see the point, though. Performance isn't anywhere near Opus, and even that gets confused following instructions or making tool calls in demanding scenarios. Open weights models are just light years behind. I really, really want open weights models to be great, but I've been disappointed with them. I don't even run them locally, I try them from providers, but they're never as good as even the current Sonne…
- Qwen3-VL picks up new images in a NAS, auto captions and adds the text descriptions as a hidden EXIF layer into the image, which is used for fast search and organization in conjunction with a Qdrant vector database.
- Gemma3:27b is used for personal translation work (mostly English and Chinese).
- Llama3.1 spins up for sentiment analysis on text.
If you're new to this: All of the open source models are playing benchmark optimization games. Every new open weight model comes with promises of being as good as something SOTA from a few months ago then they always disappoint in actual use. I've been playing with Qwen3-Coder-Next and the Qwen3.5 models since they were each released. They are impressive, but they are not performing at Sonnet 4.5 level in my experien…
Are you saying that the benchmarks are flawed? And could quantization maybe partially explain the worse than expected results?
The benchmarks are public. They're guaranteed to be in the training sets by now. So the benchmarks are no longer an indicator of general performance because the specific tasks have been seen before.
> And could quantization maybe explain the worse than expected results?
You can use the models through various providers on OpenRouter cheaply without quantization.
Earlier quoted context omitted.
Are you saying that the benchmarks are flawed? And could quantization maybe partially explain the worse than expected results?
The models outperform on the benchmarks relative to general tasks. The benchmarks are public. They're guaranteed to be in the training sets by now. So the benchmarks are no longer an indicator of general performance because the specific tasks have been seen before. > And could quantization maybe explain the worse than expected results? You can use the models through various providers on OpenRouter cheaply without qua…
If you're new to this: All of the open source models are playing benchmark optimization games. Every new open weight model comes with promises of being as good as something SOTA from a few months ago then they always disappoint in actual use. I've been playing with Qwen3-Coder-Next and the Qwen3.5 models since they were each released. They are impressive, but they are not performing at Sonnet 4.5 level in my experien…
Are you saying that the benchmarks are flawed? And could quantization maybe partially explain the worse than expected results?
Quantisation doesn't help, but even running full fat versions of these models through various cloud providers, they still don't match Sonnet in actual agentic coding uses: at least in my experience.
If you're new to this: All of the open source models are playing benchmark optimization games. Every new open weight model comes with promises of being as good as something SOTA from a few months ago then they always disappoint in actual use. I've been playing with Qwen3-Coder-Next and the Qwen3.5 models since they were each released. They are impressive, but they are not performing at Sonnet 4.5 level in my experien…
Are you saying that the benchmarks are flawed? And could quantization maybe partially explain the worse than expected results?
I have two of my own comments to add to that. First one is that there is problem alignment at play. Specifically - the benchmarks are mostly self-contained problems with well defined solutions and specific prompt language, humans tasks are open ended with messy prompts and much steerage. Second is that it would be interesting to test older models on brand new benchmarks to see how those compare.
Earlier quoted context omitted.
The hardware difference explains runtime performance differences, not task performance. Speculation is that the frontier models are all below 200B parameters but a 2x size difference wouldn’t fully explain task performance differences
Who would have thought ai labs with billions upon billions of r&d budget would have better models than a free alternative.